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“Inventory Optimization Benefits Noted Despite Challenges in System Adaptability and Costs”
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Company Description
Material Digital Intelligence Research Institute (MDIRI) develops digital and intelligent technologies to support data-driven decision making. The institute utilizes agentic AI, industry large models (ILMs), big data, and security technologies to deliver an integrated platform. This platform enables customizable decision intelligence processes—from data analysis and simulation to prediction and optimization—by incorporating enterprise business logic into ILMs. Organizations leverage these capabilities to monitor dynamic performance indicators, gain real-time operational insights, implement agile event response, and execute decisions across enterprise, supply chain, and industry levels.
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Industry Large Model Likes & Dislikes
1. The Industry Large Model enables precise demand forecasting, accurate across regions and categories, accelerating inventory turnover and reducing obsolescence. 2. The Industry Large Model integrates the end-to-end chain, dynamically optimizing inventory with automatic transfer and replenishment, boosting warehouse utilization and reducing stockouts. 3. The Industry Large Model synergizes logistics resources, intelligently scheduling routes to improve delivery efficiency, reduce empty runs, and lower carbon emissions.
The global optimization in Industry Large Model impressed me. It balances purchasing decisions, production planning, and inventory levels across our facilities.And it quickly turns planning suggestions into actual steps. What really helped was that scheduling plans no longer need manual entry into our MES, WMS, SCM systems and extra. This saved us tons of work and costs.
MDIRI's ILMs adapts well across different settings. It only needs small adjustments to roll out quickly in dozens of our diverse factories, speeding up our transformation. Functionally, ILMs operates in a closed-loop with minimal manual intervention. During implementation, MDIRI also helped us establish unified data governance, laying the foundation for deeper digital transformation.
1. ILMs are weak in extreme scenario generalization, slow to respond to abnormal data, requiring manual intervention and limiting supply chain resilience. 2. ILMs lack deep data collaboration, with system interface barriers and poor data real-time accuracy, restricting decision precision. 3. IMLS suffers from poor system stability, high-concurrency response delays, and high computing costs, making it unaffordable for SMEs.
Our supply chain is complex - millions of products and global suppliers. Deploying Mdiri's system took longer than expected. They spent nearly 7 months understanding our supply chain details and integrating Industry Large Model to our systems. It required many staff to handle the links and adjustments. I hope it will be more effective.
In our factories, process engineers are the main ILMs users. They found that at first, the ILMs took longer to learn and needed longer training before using it alone. Also, the initial deployment required setting up many parameters, which was time-consuming. Automating this setup would be better.
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Industry Large Model Reviews and Ratings
- Operation Manager10B+ USDConsumer GoodsReview Source
Inventory Optimization Benefits Noted Despite Challenges in System Adaptability and Costs
MDIRI's inventory and logistics AI applications, leveraging its "Industry Large Model" technology, demonstrate strong technical implementation capabilities - especially in intelligent demand forecasting, end-to-end dynamic inventory optimization, and logistics resource scheduling, significantly enhancing supply chain efficiency and responsiveness. However, shortcomings remain in adaptability to complex scenarios, system stability, and cross-departmental data collaboration, with certain functions limited by data quality and external environmental fluctuations. - Product Manager1B-10B USDManufacturingReview Source
Improved Production Flexibility Noted, Though Implementation Was Resource Intensive
We worked with Mdiri on production scheduling. Their Industry Large Model tied together our supply chain planning, factory production, and shop floor scheduling. This really helped - production efficiency improved, and our supply chain became more flexible. In June, we had an urgent order to insert. They handled it well without disrupting other orders or materials inventory. - Digitalization Department Manager10B+ USDManufacturingReview Source
ILMs System Accelerates Digital Transformation But Presents Training and Setup Challenges
MDIRI‘s ILMs helps our traditional businesses go digital. Over the past two years， it improved smart decision-making for dozens of our factories.This covers production, supply chain, and sales. It also saved us real money. - Manager50M-1B USDIT ServicesReview Source
Integration of Industry Large Model Improved Customer Satisfaction and AI Capabilities
We integrated the Industry Large Model into our AI solutions, enhancing intelligent capabilities. This has helped improve our customer satisfaction.Overall, MDIRI is a trusted partner for us. - Supply Chain Manager10B+ USDManufacturingReview Source
Optimized Production Scheduling Shortens Lead Times and Improves Supply Chain Visibility
MDIRI's team has significantly shortened our lead time through optimized supply chain and production scheduling. This enhancement strengthens our competitive position in the industry.